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ISSN: 2278 –
1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012 A Survey on Clustering Based Image Segmentation Santanu Bhowmik, Viki Datta Abstract – In computer vision, segmentation refers to II. CLUSTERING the process of partitioning a digital image into multiple segments (Sets of pixels, also known as super Clustering is a process of organizing the objects pixels). This paper is a survey on various clustering into groups based on its attributes. A cluster is techniques to achieve image segmentation. In order to therefore a collection of objects which are “similar” increase the efficiency of the searching process, only a between them and are “dissimilar” to the objects part of the database need to be searched. For this belonging to other clusters. An image can be searching process clustering techniques can be grouped based on keyword (metadata) or its recommended. Clustering can be termed here as a content (description). grouping of similar images in the database. Clustering is done based on different attributes of an In keyword based clustering, a keyword is a form image such as size, color, texture etc. The purpose of of font which describes about the image keyword clustering is to get meaningful result, effective storage of an image refers to its different features. The and fast retrieval in various areas. similar featured images are grouped to form a cluster by assigning value to each feature. Key Words – Clustering, Image segmentation, K- In content based clustering “[10], [11], [23]” a means, N-cut, Spectral Clustering. content refers to shapes, textures or any other information that can be inherited from the image itself. The tools, techniques and algorithms that are I. INTRODUCTION used originate from fields such as statistics, pattern Clustering in image segmentation is defined as the recognition, signal processing etc. Clustering based process of identifying groups of similar image on the optimization of an overall measure is a primitive [1]. Clustering techniques can be fundamental approach explored since the early classified into supervised clustering-demands days of pattern recognition. The most popular human interaction to decide the clustering criteria method for pattern recognition is K-means and the unsupervised clustering- decides the clustering. clustering criteria by itself. Supervised clustering includes hierarchical approaches such as relevance In K-means clustering a centroid vector is feedback techniques “[2], [3]” and unsupervised computed for every cluster. The centroid must be clustering includes density based clustering chosen such that it should minimize the total methods. These clustering techniques are done to distance within the clusters. perform image segmentation. Segmentation is the Q process of partitioning a digital image into multiple segments based on pixels. It is a critical and essential component of image analysis system. The S main process is to represent the image in a clear T VV Q way. The result of image segmentation is a collection of segments which combine to form the entire image [4]. Real world image segmentation problems actually have multiple objectives such as minimize overall deviation, maximize connectivity, P minimize the features or minimize the error rate of U the classifier etc [6]. Image segmentation is a multiple objective Figure-1 R problem. It involves several processes such as pattern representation [5], feature selection, feature extraction and pattern proximity. Considering all Figure-1 shows the preferred centroid (V) for the these objectives is a difficult problem, causing a triangle. The points namely S, T, U are the midpoint for gap between the natures of images. To bridge this corresponding edges. gap multi-objective optimization approach is an appropriate method “[7], [8], [9]”. 280 All Rights Reserved © 2012 IJARCET
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ISSN: 2278 –
1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012 Both supervised and unsupervised clustering IV. CLUSTERING TECHNIQUES techniques are used in image segmentation. In An image may contain more than one object and to supervised clustering method, grouping is done segment the image in line with object features to according to user feedback. In unsupervised extract meaningful object has become a challenge clustering, the images with high features to the researches in the field. Segmentation can be similarities to the query may be very different in achieved through clustering. terms of semantics [16]. This is known as semantic This paper critically reviews and summarizes gap. To overcome this novel image retrieval different clustering techniques. scheme called as cluster based retrieval of images by unsupervised learning (CLUE) can be used [17]. This works based on a hypothesis: semantically IV.1. Relevance feedback: similar images tend to be clustered in some feature A relevance feedback approach allows a space. user to interact with the retrieval algorithm by A variety of clustering techniques have been providing the information of which images user introduced to make the segmentation more thinks are relevant to the query effective. The clustering techniques which are “[28],[29],[30]”.Keyword based image retrieval is included in this paper are relevance feedback [13], performed by matching keyword according to user log based clustering [14], hierarchical clustering input and the images in the database. [15], graph based, retrieval-dictionary based, filter Some images may not have appropriate keywords based clustering etc. to describe them and therefore the image search will become complex. One of the solution in order to overcome this problem is “relevance feedback” III. SEGMENTATION technique [41] that utilize user feedback and hence Image segmentation is the important process of reduces possible errors and redundancy “[31], [3]”. image analysis and image understanding [18]. It is This technique uses a Bayesian classifier “[12], defined as the process of partitioning the digital [39]” which deals with positive and negative image into different sub regions of homogeneity. feedback. Content based clustering methods cannot The objective of image segmentation is to cluster adapt to user changes, addition of new topics due to pixels into salient image regions i.e., regions its static nature. To improve the performance of corresponding to individual surfaces, objects or information retrieval log-based clustering natural parts of objects. approaches are brought into the application. A segmentation might be used for object recognition “[19], [20]” image compression, image IV.2. Log –Based Clustering: editing, etc. The quality of the segmentation depends upon the digital image [21]. In the case of Images can be clustered based on the simple images the segmentation process is clear retrieval system logs maintained by an information and effective due to small pixels variations, retrieval process [11]. The session keys are created whereas in the case of complex images, the utility and accessed for retrieval. Through this the session for subsequent processing becomes questionable. clusters are created. Each session cluster generates Image segmentation is one of the best known log –based document and similarity of image problems in computer vision. Graph based methods couple is retrieved. Log –based vector is created for were earlier considered to be too insufficient in each session vector based on the log-based practice. Recent advances in technology and documents [40]. Now, the session cluster is algorithm “[22], [18]” have negated this replaced with this vector. The unaccessed assumption. Histogram “[24], [25], [26]” based document creates its own vector. methods are very effective while compared to other A hybrid matrix is generated with at least one image segmentation methods because they individual document vector and one log-based typically require only one pass through the pixels. clustered vector. At last the hybrid matrix is In this method a histogram is computed from all of clustered. This technique is difficult to perform in the pixels in the image and the peaks and valleys in the case of multidimensional images. To overcome the histogram are used to locate the clustering of this hierarchical clustering is adopted. the image. Intensity can be used as the measure. This process is repeated with smaller and smaller IV.3. Hierarchical Clustering: clusters until no more clusters are formed. This approach can be quickly adapted to multiple frames One of the well- known technologies in which is done in multiple fashion. information retrieval is hierarchical clustering [15]. It is the process of integrating different images and Segmentation can also be done based on spatial coherence [27]. This includes two steps: Dividing building them as a cluster in the form of a tree and or merging existing regions from the image and then developing step by step in order to form a small cluster. growing regions from seed points. 281 All Rights Reserved © 2012 IJARCET
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1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012 The steps involved in this process are as follows: terminates when the bound on the number of the images from various databases are divided into clusters is reached or the Ncut value exceeds some X-sorts. The classification will be calculated by threshold T. modifying the cluster centers, sorts of the images and stored in the form of matrix m*m continuously 200 which also includes dissimilarity values. At first it calculates the similarities between the queried V image and the retrieved image in the image 70 database. Secondly, it identifies the similarities 130 between two closest images(In m*m matrix)and C1 integrate them to form a cluster. Finally all the C2 similarities are grouped to form a single cluster. 50 20 75 55 IV.4. Retrieval Dictionary Based Clustering: A rough classification retrieval system is C7 C3 C8 C4 formed. This is formed by calculating the distance between two learned patterns and these learned patterns are classified into different clusters 30 45 followed by a retrieval stage. The main drawback addressed in this system is the determination of the C5 C6 distance. To overcome this problem a retrieval system is Figure-2 developed by retrieval dictionary based clustering [33]. This method has a retrieval dictionary Figure-2 shows Ncut Nodes organized as groups. generation unit that classifies learned patterns into plural clusters and creates a retrieval dictionary The recursive Ncut partition is essentially a using the clusters. Here, the image is retrieved hierarchical divisive clustering process that based on the distance between two spheres with produces a tree [37]. For example, Figure 2 shows different radii. Each radius is a similarity measure a tree generated by four Recursive Ncuts. The first between central cluster and an input image. An Ncut divides V into C1 and C2. Since C2 is larger image which is similar to the query image will be than C1, the second Ncut partitions C2 into C3 and retrieved using retrieval dictionary. C4.Next, C3 is further divided because it is larger than C1 and C4. The fourth Ncut is applied to C1, and gives the final five clusters (or leaves): C4, C5, IV.5. K-Means Algorithm: C6, C7 and C8.The above example suggest trees as In K-means algorithm data vectors are a natural organization of clusters [35]. Nonetheless, grouped into predefined number of clusters [32] the tree organization here may mislead a user [33]. At the beginning the centroids of the because there is no guarantee of any predefined clusters are initialized randomly. The correspondence between the tree and the semantic dimensions of the centroids are same as the structure of images. Furthermore, organizing image dimension of the data vectors. Each pixel is clusters into a tree structure will significantly assigned to the cluster based on the closeness [34], complicate the user interface. which is determined by the Euclidian distance measure. After all the pixels are clustered, the mean of each cluster is recalculated. This process is V. CONCLUSOIN repeated until no significant changes result for each To summarize, a comprehensive survey cluster mean or for some fixed number of highlighting different clustering techniques used iterations. for image segmentation have been presented. Clustering concepts and image segmentation IV.6. Ncut Algorithm: concepts have been analyzed. Through clustering algorithms, image segmentation can be done in an Ncut method attempts to organize nodes effective way. Spectral clustering technique can be into groups so that the within the group similarity is used for image clustering because images that high, and/or between the groups similarity is low. cannot be seen can be placed into clusters very This method is empirically shown to be relatively easily than other traditional methods [38]. In robust in image segmentation [36]. This method general, clustering is a hard problem. Clustering can be recursively applied to get more than two techniques helps to increase the efficiency of the clusters. In this method each time the sub graph image retrieval process. with maximum number of nodes is partitioned (random selection for tie breaking). The process 282 All Rights Reserved © 2012 IJARCET
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1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012 Authors – Mr. Santanu Bhowmik, M.Tech, MCA, Ph.D Scholar, NIT Agartala Shan.bhowmik7@gmail.com. Mr. Viki Datta, MCA Technical Asstt., NIT Agartala vdatta49@gmail.com 284 All Rights Reserved © 2012 IJARCET
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